Elsevier · 11 hours ago
Senior Software Engineer - Retrieval-Augmented Generation (RAG) System
Philadelphia, PA
Full-time
Onsite
Mid, Senior Level
$87K/yr - $144K/yr
5+ years expElsevier is a renowned global information analytics company that primarily focuses on providing scientific, technical, and medical (STM) research content, tools, and services. They are seeking an engineer to work with a team to build and support a healthcare-centered production-scale RAG system that combines document retrieval with response generation to deliver accurate, context-aware answers.
ContentContent DiscoveryDeliveryHealth CareInformation ServicesInformation TechnologyPublishing
Responsibilities
Architecting, implementing, testing, and operating end-to-end RAG workflows:
Ingesting and normalizing documents from diverse sources
Generating and managing embeddings; index and query vector databases Retrieve relevant passages, apply reranking or fusion strategies, and feed prompts to LLMs
Building scalable, low-latency services and APIs (Python preferred; other languages acceptable) and ensure production-grade reliability (monitoring, tracing, alerting)
Integrating with vector databases and embedding pipelines and optimize for latency, throughput, and cost
Designing and implementing ML Ops workflows: model/version management, experiments, feature stores, CI/CD for ML-enabled services, rollback plans
Developing robust data pipelines and governance around ingestion, provenance, quality checks, and access controls
Collaborating with data engineers to improve retrieval quality (embedding strategies, reranking, cross-encoder models, prompt engineering) and implement evaluation metrics (precision/recall, MRR, QA accuracy, user-centric metrics)
Implementing monitoring and observability for RAG components (latency, success rate, cache hit rate, retrieval quality, data drift)
Ensuring security, privacy, and compliance (authentication, authorization, data masking, PII handling, audit logging)
Qualification
Required
5+ years of professional software engineering experience designing and delivering production systems
Strong programming skills (Python required; NodeJs a plus)
Deep understanding of retrieval-augmented or application-scale NLP systems and practical experience building RAG-like pipelines
Hands-on experience with ML workflow tooling and MLOps concepts (model serving, versioning, experiments, feature stores, reproducibility)
Proficiency with cloud infrastructure and modern software practices (AWS/GCP/Azure; Docker; Kubernetes; CI/CD)
Strong problem-solving skills, excellent communication, and ability to work with cross-functional teams
Familiarity with data governance, privacy, and security best practices
Preferred
Experience with agentic workflow tools (LangGraph) and familiarity with prompt engineering for LLMs
Exposure to working with and evaluating different LLMs
Knowledge of evaluation methodologies for retrieval and QA systems and the ability to set up A/B tests and dashboards
Experience with data processing frameworks (SQL, Pandas, Spark) and working with large-scale data pipelines
Background in performance optimization for low-latency AI services (MLflow)
Experience with monitoring and logging via New Relic, K9s, Portkey, etc
Experience with minimizing token usage and cost optimization
Comfortable with design and implementation of security controls for data-intensive AI systems
Benefits
Annual incentive bonus
Country specific benefits
Company
Elsevier
Elsevier is a world-leading provider of information solutions that enhance the performance of science, health, and technology. It is a sub-organization of RELX.
5001-10000 employees
H1B Sponsorship
Elsevier has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (32)
2024 (17)
2023 (28)
2022 (46)
2021 (28)
2020 (19)
Funding
Current Stage
Late StageTotal Funding
unknown2003-09-01Private Equity
Leadership Team
Dan Olley
EVP & CTO - Elsevier
C
Catherine Thrift
CFO
Recent News
News-Medical.Net
2026-01-16
2025-12-18
Business Wire
2025-12-17
Company data provided by crunchbase